Natural Language Processing

Natural Language Processing

Faction XYZ has made it its mission to push the boundaries of what is possible with Natural Language Processing. We make a difference by applying computational linguistics to solve big, real world problems. From classification, prediction, information extraction to personality, demographical and mood analysis.

Why now?

One of the most interesting subfields of Artificial Intelligence is Natural Language Processing. Modern processing power and new techniques have made an enormous amount of use cases possible that were science fiction up to a couple of years ago. Faction XYZ has made it its mission to push the boundaries of what is possible with NLP. Our conversational intelligence platform is the most publicly visible result of that.

What can we do?

We love natural language processing and we especially love doing things with it that are useful in the real world.

  • Classifying and tagging text. Whether the data source is tweets, e-mails, documents, website or books, you want to be able to better search, quantify and gain insight in your existing and future collections. At Faction XYZ, we do research on benchmark breaking NLP models.
  • Regression and prediction. Unstructured text can be used as an additional input for regular predictive models. Predictions from unstructured text are used in insurance claim processing, AI jury members, predictive maintenance on inspection logs, etc.
  • Information extraction. It is a sad given that most of the information in the world is in an unstructured form. Recent advances in information extraction however have made it feasible to automate large parts of the information extraction process. By using ontologies and parsing trees, Machine Learning models can find connections and patterns between words with a high degree of accuracy. This saves companies immense amounts of time by having the right information in a structured format handy.
  • Automatic response generation. Knowing what people are saying is great, but being able to respond semi-automatically or fully automatically is where you can save time and money. Depending on the use case, AI can be used to suggest answers, choose the correct answer from a number of predefined templates, or fully generate text itself.
  • Voice, translation, transcribing. We do not have experience in building these models. We do however have worked with 3rd-party models a lot and have successfully integrated them with multiple applications. We believe there is no use in reinventing the wheel, which is why we rely on 3rd-party models for voice, translation and transcription.
  • Personality, demographical and mood analysis. All people write in different ways. By analyzing their writing patterns, emoji use, vocabulary, etc., you can reasonably estimate a person’s age, gender, level of education, even his or her mood at the moment. This data can be used for personalized advertising, gaining more insight or reacting in a more personal way.